Library & Dataset

Using OLR

Inspect Dataset Using Training and Validation

OLR Equations

Using all variables

Inspect Dataset Using Training and Validation

Training set divide in the Diagnostic_based.on.Harell.Rmd file

vclust <- varclus (~angle+brick+wood+mixed+ density+EN +TC + TC_mature_soil + TC_saprolite_soil +  TC_weath_rock  + TC_unstable_structure  + T_construction  + spring +  landfill + garbage  + crack + leaning_wall + scars + downward_floor + tilted + fracture + conc_rainfall + wastewater + leak + septic_tank  + tree + ground_veg + deforestation + banana + drainage , data=train.data)

# took out density since training has 0 d4 and it was not allowing do the plot

p <- plot(vclust)

par(mfrow=c(6,5))
plot.xmean.ordinaly (risk~angle+brick+wood+mixed+ density+EN +TC + TC_mature_soil + TC_saprolite_soil +  TC_weath_rock  + TC_unstable_structure  + T_construction  + spring +  landfill + garbage  + crack + leaning_wall + scars + downward_floor + tilted + fracture + conc_rainfall + wastewater + leak + septic_tank  + tree + ground_veg + deforestation + banana + drainage, data=train.data, cr=TRUE , subn=FALSE)

#angle + building+density+EN +TC + TC_mature_Soil + TC_saprolito +  TC_weath_rock + TC_rock + TC_geol_desfav + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + DepTaludeAterro + aterro + lixo + entulho + crack + belly_wall + scars + drawback + tilted + fracture + conc_rainfall_water + wastewater + leak + septic_tank + drainage + tree + ground_veg + deforestation + banana

Diagnostic 2: Proportion (-5% of one of the parameters based on what is expected. Since some parameters have 2 predictors, others 5)

#library(plyr)
brick <- count(train.data$brick) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "brick")

wood <- count(train.data$wood) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "wood")

mixed <- count(train.data$mixed) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "mixed")

TC_mature_soil <- count(train.data$TC_mature_soil) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_mature_soil")

T_construction  <- count(train.data$T_construction ) %>%
  mutate ("Percentage"=(freq/265)*100) %>%
  mutate("Classifier" = "T_construction ")

spring <- count(train.data$spring) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "spring")

landfill <- count(train.data$landfill) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "landfill")

garbage <- count(train.data$garbage) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "garbage")

crack <- count(train.data$crack) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "crack")

leaning_wall <- count(train.data$leaning_wall) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "leaning_wall")

scars <- count(train.data$scars) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "DepTaludeAterro")

downward_floor <- count(train.data$downward_floor) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "scars")

tilted <- count(train.data$tilted) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "tilted")

conc_rainfall <- count(train.data$conc_rainfall) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "conc_rainfall")

wastewater <- count(train.data$wastewater) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "wastewater")

leak <- count(train.data$leak) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "conc_rainfall_water")

septic_tank <- count(train.data$septic_tank) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "septic_tank")

angle <- count(train.data$angle) # angle A less than 5% but the rest are okay (3,50, 91, 277, 109) Expected=106
angle <- angle %>%
  mutate("Percentage"=(freq/106)*100)%>%
  mutate("Classifier" = "angle")

EN <- count(train.data$EN) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "EN")

TC <- count(train.data$TC)  %>%
  mutate ("Percentage"=(freq/265)*100) %>%
  mutate("Classifier" = "TC")

TC_saprolite_soil  <- count(train.data$TC_saprolite_soil )  %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_saprolite_soil ")

banana <- count(train.data$banana) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "banana")

drainage <- count(train.data$drainage) %>%
  mutate ("Percentage"=(freq/176.7)*100)%>%
  mutate("Classifier" = "drainage")

deforestation <- count(train.data$deforestation) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "deforestation")

TC_unstable_structure  <- count(train.data$TC_unstable_structure ) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_unstable_structure ")


tree <- count(train.data$tree) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "tree")

ground_veg <- count(train.data$ground_veg) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "ground_veg")


density <- count(train.data$density)  %>% #(79, 415, 36) # d4 =0 
  mutate ("Percentage"=(freq/132.5)*100)%>%
  mutate("Classifier" = "density")

TC_weath_rock  <- count(train.data$TC_weath_rock ) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_weath_rock ")

fracture <- count(train.data$fracture) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "fracture")









df <- rbind(brick, wood, mixed, TC_mature_soil, T_construction, spring, landfill, garbage, crack, leaning_wall, scars, downward_floor, tilted, conc_rainfall, wastewater, leak, septic_tank, angle, EN, TC, TC_saprolite_soil,  banana, drainage, deforestation, TC_unstable_structure, tree, ground_veg,density, TC_weath_rock, fracture)

df

##        x freq  Percentage             Classifier
## 1  FALSE   33  12.4528302                  brick
## 2   TRUE  497 187.5471698                  brick
## 3  FALSE  459 173.2075472                   wood
## 4   TRUE   71  26.7924528                   wood
## 5  FALSE  489 184.5283019                  mixed
## 6   TRUE   41  15.4716981                  mixed
## 7  FALSE  257  96.9811321         TC_mature_soil
## 8   TRUE  273 103.0188679         TC_mature_soil
## 9  FALSE  220  83.0188679        T_construction 
## 10  TRUE  310 116.9811321        T_construction 
## 11 FALSE  513 193.5849057                 spring
## 12  TRUE   17   6.4150943                 spring
## 13 FALSE  330 124.5283019               landfill
## 14  TRUE  200  75.4716981               landfill
## 15 FALSE  350 132.0754717                garbage
## 16  TRUE  180  67.9245283                garbage
## 17 FALSE  437 164.9056604                  crack
## 18  TRUE   93  35.0943396                  crack
## 19 FALSE  498 187.9245283           leaning_wall
## 20  TRUE   32  12.0754717           leaning_wall
## 21 FALSE  319 120.3773585        DepTaludeAterro
## 22  TRUE  211  79.6226415        DepTaludeAterro
## 23 FALSE  471 177.7358491                  scars
## 24  TRUE   59  22.2641509                  scars
## 25 FALSE  437 164.9056604                 tilted
## 26  TRUE   93  35.0943396                 tilted
## 27 FALSE   17   6.4150943          conc_rainfall
## 28  TRUE  513 193.5849057          conc_rainfall
## 29 FALSE  199  75.0943396             wastewater
## 30  TRUE  331 124.9056604             wastewater
## 31 FALSE  355 133.9622642    conc_rainfall_water
## 32  TRUE  175  66.0377358    conc_rainfall_water
## 33 FALSE  526 198.4905660            septic_tank
## 34  TRUE    4   1.5094340            septic_tank
## 35     C   30  28.3018868                  angle
## 36     D  132 124.5283019                  angle
## 37     E  368 347.1698113                  angle
## 38 FALSE  338 127.5471698                     EN
## 39  TRUE  192  72.4528302                     EN
## 40 FALSE   31  11.6981132                     TC
## 41  TRUE  499 188.3018868                     TC
## 42 FALSE  441 166.4150943     TC_saprolite_soil 
## 43  TRUE   89  33.5849057     TC_saprolite_soil 
## 44 FALSE  355 133.9622642                 banana
## 45  TRUE  175  66.0377358                 banana
## 46     Y   71  40.1810979               drainage
## 47     P  232 131.2959819               drainage
## 48     N  227 128.4663271               drainage
## 49 FALSE  492 185.6603774          deforestation
## 50  TRUE   38  14.3396226          deforestation
## 51 FALSE  515 194.3396226 TC_unstable_structure 
## 52  TRUE   15   5.6603774 TC_unstable_structure 
## 53 FALSE  218  82.2641509                   tree
## 54  TRUE  312 117.7358491                   tree
## 55 FALSE  150  56.6037736             ground_veg
## 56  TRUE  380 143.3962264             ground_veg
## 57    d1   67  50.5660377                density
## 58    d2  423 319.2452830                density
## 59    d3   40  30.1886792                density
## 60 FALSE  518 195.4716981         TC_weath_rock 
## 61  TRUE   12   4.5283019         TC_weath_rock 
## 62 FALSE  529 199.6226415               fracture
## 63  TRUE    1   0.3773585               fracture

TC_weath_rock, TC_rock_TC_geol_desf, fracture, TC_rock

OF: angle (slope angle), building (building material), EN (natural slope), TC_saprolito (cut slope with saprolite), Taterro (slope with landfill), DepEncostaNatural (natural slope with construction material), DepTaludeAterro (slope with landfill and construction material), DepTaludeCorte (cut slope with construction material), landfill, garbage, construction_deposit (construction material), crack, leaning_wall, scars, downward_floor, tilted, conc_rainfall_water (concentration of rainfall water), wastewater, leak, drainage
MOF:TC_mature_soil (slope cut with soil), density, TC (slope cut), - - TC_geol_desfav (cut slope with geology favorable to instability), tree, ground_veg (ground vegetation), deforestation, banana
NOF: TC_weath_rock (cut slope with weathering rock), TC_rock (cut slope in bedrock), fracture, septic_tank
GP:all classifiers minus BP classifiers
BP: TC_weath_rock (True N= 11 , False N= 529), TC_geol_desf (True N= 11 , False N= 529), fracture (True N=1 , False N=529), TC_rock (True N=1 , False N=529 ), septic_tank (True N=4 , False N=526 )

Equation 1

consider all parameter okay from first diagnostic:

f1 <- lrm(risk ~ building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + tree + ground_veg + banana , data=train.data, x=TRUE , y=TRUE)

# Equation 1

eq_OLR_01 <- polr(risk ~ brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil, data= train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_01))



p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                             Value Std. Error    t value      p value
## brickTRUE             -0.90571356  0.4482420 -2.0205906 2.166108e-02
## woodTRUE               1.19305709  0.3227843  3.6961434 1.094497e-04
## ENTRUE                 0.36995285  0.3554549  1.0407870 1.489872e-01
## TC_mature_soilTRUE     0.63590612  0.2158958  2.9454304 1.612529e-03
## T_constructionTRUE     0.72925151  0.3581180  2.0363443 2.085789e-02
## springTRUE            -0.19379492  0.6488691 -0.2986657 3.825976e-01
## landfillTRUE          -0.12227217  0.3266681 -0.3743009 3.540902e-01
## leakTRUE              -0.24443428  0.2349938 -1.0401732 1.491297e-01
## garbageTRUE           -0.08640315  0.2886514 -0.2993340 3.823426e-01
## crackTRUE              1.56036886  0.3230498  4.8301188 6.822579e-07
## leaning_wallTRUE       1.41245087  0.4798512  2.9435187 1.622521e-03
## scarsTRUE              3.62424756  0.3269981 11.0833900 7.552161e-29
## downward_floorTRUE     1.05677057  0.3701001  2.8553642 2.149375e-03
## tiltedTRUE             0.95046871  0.3278435  2.8991535 1.870858e-03
## septic_tankTRUE       -0.15096733  1.1541543 -0.1308034 4.479654e-01
## conc_rainfallTRUE      1.81064328  0.5714170  3.1686900 7.656380e-04
## wastewaterTRUE         0.84821836  0.2324285  3.6493726 1.314408e-04
## ground_vegTRUE         0.86828266  0.2523118  3.4413081 2.894545e-04
## angleD                 0.39301030  0.4680075  0.8397521 2.005237e-01
## angleE                 0.46510674  0.5275722  0.8815983 1.889970e-01
## TC_saprolite_soilTRUE  0.22104204  0.2762444  0.8001683 2.118066e-01
## R1|R2                  0.92985845  0.9040600  1.0285363 1.518488e-01
## R2|R3                  5.13398847  0.9456596  5.4290024 2.833497e-08
## R3|R4                  9.79257161  1.0345879  9.4651902 1.465136e-21

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ======================================================
##                       Value Std. Error t value p value
## ------------------------------------------------------
## brickTRUE             -0.91    0.45     -2.02   0.02  
## woodTRUE              1.19     0.32     3.70   0.0001 
## ENTRUE                0.37     0.36     1.04    0.15  
## TC_mature_soilTRUE    0.64     0.22     2.95    0.002 
## T_constructionTRUE    0.73     0.36     2.04    0.02  
## springTRUE            -0.19    0.65     -0.30   0.38  
## landfillTRUE          -0.12    0.33     -0.37   0.35  
## leakTRUE              -0.24    0.23     -1.04   0.15  
## garbageTRUE           -0.09    0.29     -0.30   0.38  
## crackTRUE             1.56     0.32     4.83   0.0000 
## leaning_wallTRUE      1.41     0.48     2.94    0.002 
## scarsTRUE             3.62     0.33     11.08     0   
## downward_floorTRUE    1.06     0.37     2.86    0.002 
## tiltedTRUE            0.95     0.33     2.90    0.002 
## septic_tankTRUE       -0.15    1.15     -0.13   0.45  
## conc_rainfallTRUE     1.81     0.57     3.17    0.001 
## wastewaterTRUE        0.85     0.23     3.65   0.0001 
## ground_vegTRUE        0.87     0.25     3.44   0.0003 
## angleD                0.39     0.47     0.84    0.20  
## angleE                0.47     0.53     0.88    0.19  
## TC_saprolite_soilTRUE 0.22     0.28     0.80    0.21  
## R1| R2                0.93     0.90     1.03    0.15  
## R2| R3                5.13     0.95     5.43   0.0000 
## R3| R4                9.79     1.03     9.47      0   
## ------------------------------------------------------

less p-value = 0.10 (TC_saprolitoTRUE,TaterroTRUE, DepTaludeAterroTRUE,DepTaludeAterroTRUE,landfillTRUE, construction_depositTRUE, leakTRUE)

par(mfrow=c(5,4))
plot.xmean.ordinaly (risk~ brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil
          ,data=train.data, cr=TRUE , subn=FALSE ,  cex.lab=1.5, cex.axis=2, cex.sub=2, cex.main=2)

Creating function with four level

Equation 1

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +-----------------+---+---+----+----------+------------+----------+
## |                 |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +-----------------+---+---+----+----------+------------+----------+
## |brick            |No | 33|Inf | 3.4657359| 1.139434283|-0.8329091|
## |                 |Yes|496|Inf | 2.2806062|-0.072612533|-2.0217759|
## +-----------------+---+---+----+----------+------------+----------+
## |wood             |No |458|Inf | 2.2167851|-0.157530203|-2.2670784|
## |                 |Yes| 71|Inf | 3.5409593| 1.079920156|-0.6097656|
## +-----------------+---+---+----+----------+------------+----------+
## |EN               |No |337|Inf | 1.8959830|-0.415366179|-2.2545759|
## |                 |Yes|192|Inf | 4.1431347| 0.740400065|-1.4663371|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_mature_soil   |No |257|Inf | 1.9151385|-0.274076420|-2.3685431|
## |                 |Yes|272|Inf | 2.9139023| 0.251314428|-1.5918936|
## +-----------------+---+---+----+----------+------------+----------+
## |T_construction   |No |220|Inf | 1.5349354|-0.804372816|-2.9444390|
## |                 |Yes|309|Inf | 3.6276687| 0.550726841|-1.4863778|
## +-----------------+---+---+----+----------+------------+----------+
## |spring           |No |512|Inf | 2.2918898|-0.031252544|-2.0005935|
## |                 |Yes| 17|Inf |       Inf| 0.875468737|-0.3566749|
## +-----------------+---+---+----+----------+------------+----------+
## |landfill         |No |329|Inf | 1.8423117|-0.502728414|-2.5422579|
## |                 |Yes|200|Inf | 4.5951199| 0.847297860|-1.2656664|
## +-----------------+---+---+----+----------+------------+----------+
## |leak             |No |354|Inf | 2.0890110|-0.284387177|-2.2749336|
## |                 |Yes|175|Inf | 3.0385523| 0.575364145|-1.3862944|
## +-----------------+---+---+----+----------+------------+----------+
## |garbage          |No |350|Inf | 2.0476928|-0.252765807|-2.3312039|
## |                 |Yes|179|Inf | 3.2015843| 0.490022496|-1.3449091|
## +-----------------+---+---+----+----------+------------+----------+
## |crack            |No |436|Inf | 2.1620451|-0.371176035|-2.6790627|
## |                 |Yes| 93|Inf | 3.8177123| 2.508437147|-0.2814125|
## +-----------------+---+---+----+----------+------------+----------+
## |leaning_wall     |No |497|Inf | 2.2591000|-0.116832917|-2.1255326|
## |                 |Yes| 32|Inf |       Inf| 2.708050201|-0.1251631|
## +-----------------+---+---+----+----------+------------+----------+
## |scars            |No |318|Inf | 1.7771607|-1.398128819|-4.1367653|
## |                 |Yes|211|Inf | 5.3471075| 3.000719815|-0.8540775|
## +-----------------+---+---+----+----------+------------+----------+
## |downward_floor   |No |470|Inf | 2.1972246|-0.248072934|-2.2454267|
## |                 |Yes| 59|Inf |       Inf| 4.060443011|-0.4480247|
## +-----------------+---+---+----+----------+------------+----------+
## |tilted           |No |436|Inf | 2.1375053|-0.399734433|-2.4386134|
## |                 |Yes| 93|Inf | 4.5217886| 3.102342009|-0.5978370|
## +-----------------+---+---+----+----------+------------+----------+
## |septic_tank      |No |525|Inf | 2.3194631|-0.011428696|-1.9221766|
## |                 |Yes|  4|Inf |       Inf| 1.098612289|-1.0986123|
## +-----------------+---+---+----+----------+------------+----------+
## |conc_rainfall    |No | 17|Inf |-0.6061358|-2.772588722|      -Inf|
## |                 |Yes|512|Inf | 2.5818989| 0.054701136|-1.8763169|
## +-----------------+---+---+----+----------+------------+----------+
## |wastewater       |No |199|Inf | 1.6522579|-0.460215623|-2.9391619|
## |                 |Yes|330|Inf | 3.0445224| 0.268263987|-1.5453591|
## +-----------------+---+---+----+----------+------------+----------+
## |ground_veg       |No |150|Inf | 1.3451363|-1.265666373|-2.5365787|
## |                 |Yes|379|Inf | 3.1218141| 0.445205437|-1.7315003|
## +-----------------+---+---+----+----------+------------+----------+
## |angle            |C  | 30|Inf |       Inf|-0.268263987|      -Inf|
## |                 |D  |132|Inf | 3.7612001| 0.762140052|-1.3581235|
## |                 |E  |367|Inf | 1.9934627|-0.246471804|-2.0733253|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_saprolite_soil|No |440|Inf | 2.2753894|-0.100083459|-2.0541237|
## |                 |Yes| 89|Inf | 2.6270811| 0.480972661|-1.3723081|
## +-----------------+---+---+----+----------+------------+----------+
## |Overall          |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +-----------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=1, cex.axis=1, cex.sub=1)

Equation 2

parameters okay and so/so
porportion
excluded TC_geol_desf

f2 <- lrm(risk ~ angle + building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + drainage + TC_mature_Soil + density + TC + tree +ground_veg + deforestation + banana , data=train.data, x=TRUE , y=TRUE)

      stargazer(anova(f2), type="text", style="default")

eq_OLR_02 <- polr(risk ~ brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation,
                  
                 data= train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_02))








p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                             Value Std. Error     t value      p value
## brickTRUE             -0.62673029  0.5293559 -1.18394885 1.182167e-01
## woodTRUE               1.13513842  0.3339284  3.39934645 3.377355e-04
## ENTRUE                 0.39002345  0.3725482  1.04690748 1.475711e-01
## TC_mature_soilTRUE     0.60913390  0.2263222  2.69144601 3.557151e-03
## T_constructionTRUE     0.82676166  0.3651228  2.26433888 1.177664e-02
## landfillTRUE          -0.23144077  0.3324566 -0.69615343 2.431664e-01
## leakTRUE              -0.37734521  0.2417603 -1.56082399 5.928264e-02
## garbageTRUE           -0.08766765  0.2914036 -0.30084615 3.817659e-01
## crackTRUE              1.58000415  0.3270524  4.83104339 6.790970e-07
## leaning_wallTRUE       1.48227476  0.4906912  3.02078917 1.260584e-03
## treeTRUE              -0.46223047  0.2376718 -1.94482676 2.589792e-02
## downward_floorTRUE     0.98664984  0.3716125  2.65504987 3.964834e-03
## tiltedTRUE             0.87246750  0.3259929  2.67633909 3.721565e-03
## ground_vegTRUE         0.88122670  0.2741453  3.21445088 6.534714e-04
## scarsTRUE              3.64162056  0.3341436 10.89836889 5.867095e-28
## mixedTRUE              0.23630421  0.4917956  0.48049272 3.154385e-01
## conc_rainfallTRUE      1.52382524  0.6076985  2.50753506 6.078826e-03
## wastewaterTRUE         0.72159287  0.2391811  3.01693050 1.276742e-03
## angleD                 0.29609771  0.4715647  0.62790480 2.650332e-01
## angleE                 0.39302852  0.5307278  0.74054630 2.294843e-01
## bananaTRUE             0.29392333  0.2470592  1.18968777 1.170846e-01
## drainage.L             0.71097588  0.2711687  2.62189527 4.372115e-03
## drainage.Q             0.03053974  0.1860008  0.16419145 4.347902e-01
## TC_saprolite_soilTRUE  0.20875355  0.2830427  0.73753388 2.303989e-01
## TCTRUE                 0.02300363  0.4820458  0.04772083 4.809694e-01
## deforestationTRUE      0.37058436  0.3942701  0.93992521 1.736280e-01
## R1|R2                  0.63293154  1.0990887  0.57586939 2.823517e-01
## R2|R3                  4.99470264  1.1214981  4.45359887 4.222140e-06
## R3|R4                  9.70510422  1.2065145  8.04391847 4.350521e-16

stargazer((ctable), type="text", style="default", digits=2)

## 
## ======================================================
##                       Value Std. Error t value p value
## ------------------------------------------------------
## brickTRUE             -0.63    0.53     -1.18   0.12  
## woodTRUE              1.14     0.33     3.40   0.0003 
## ENTRUE                0.39     0.37     1.05    0.15  
## TC_mature_soilTRUE    0.61     0.23     2.69    0.004 
## T_constructionTRUE    0.83     0.37     2.26    0.01  
## landfillTRUE          -0.23    0.33     -0.70   0.24  
## leakTRUE              -0.38    0.24     -1.56   0.06  
## garbageTRUE           -0.09    0.29     -0.30   0.38  
## crackTRUE             1.58     0.33     4.83   0.0000 
## leaning_wallTRUE      1.48     0.49     3.02    0.001 
## treeTRUE              -0.46    0.24     -1.94   0.03  
## downward_floorTRUE    0.99     0.37     2.66    0.004 
## tiltedTRUE            0.87     0.33     2.68    0.004 
## ground_vegTRUE        0.88     0.27     3.21    0.001 
## scarsTRUE             3.64     0.33     10.90     0   
## mixedTRUE             0.24     0.49     0.48    0.32  
## conc_rainfallTRUE     1.52     0.61     2.51    0.01  
## wastewaterTRUE        0.72     0.24     3.02    0.001 
## angleD                0.30     0.47     0.63    0.27  
## angleE                0.39     0.53     0.74    0.23  
## bananaTRUE            0.29     0.25     1.19    0.12  
## drainage.L            0.71     0.27     2.62    0.004 
## drainage.Q            0.03     0.19     0.16    0.43  
## TC_saprolite_soilTRUE 0.21     0.28     0.74    0.23  
## TCTRUE                0.02     0.48     0.05    0.48  
## deforestationTRUE     0.37     0.39     0.94    0.17  
## R1| R2                0.63     1.10     0.58    0.28  
## R2| R3                4.99     1.12     4.45   0.0000 
## R3| R4                9.71     1.21     8.04      0   
## ------------------------------------------------------

par(mfrow=c(6,4))
plot.xmean.ordinaly (risk~ brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation
          ,data=train.data, cr=TRUE , subn=FALSE ,  cex.lab=1.5, cex.axis=4, cex.sub=4, cex.main=4)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation,data=train.data
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +-----------------+---+---+----+----------+------------+----------+
## |                 |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +-----------------+---+---+----+----------+------------+----------+
## |brick            |No | 33|Inf | 3.4657359| 1.139434283|-0.8329091|
## |                 |Yes|496|Inf | 2.2806062|-0.072612533|-2.0217759|
## +-----------------+---+---+----+----------+------------+----------+
## |wood             |No |458|Inf | 2.2167851|-0.157530203|-2.2670784|
## |                 |Yes| 71|Inf | 3.5409593| 1.079920156|-0.6097656|
## +-----------------+---+---+----+----------+------------+----------+
## |EN               |No |337|Inf | 1.8959830|-0.415366179|-2.2545759|
## |                 |Yes|192|Inf | 4.1431347| 0.740400065|-1.4663371|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_mature_soil   |No |257|Inf | 1.9151385|-0.274076420|-2.3685431|
## |                 |Yes|272|Inf | 2.9139023| 0.251314428|-1.5918936|
## +-----------------+---+---+----+----------+------------+----------+
## |T_construction   |No |220|Inf | 1.5349354|-0.804372816|-2.9444390|
## |                 |Yes|309|Inf | 3.6276687| 0.550726841|-1.4863778|
## +-----------------+---+---+----+----------+------------+----------+
## |landfill         |No |329|Inf | 1.8423117|-0.502728414|-2.5422579|
## |                 |Yes|200|Inf | 4.5951199| 0.847297860|-1.2656664|
## +-----------------+---+---+----+----------+------------+----------+
## |leak             |No |354|Inf | 2.0890110|-0.284387177|-2.2749336|
## |                 |Yes|175|Inf | 3.0385523| 0.575364145|-1.3862944|
## +-----------------+---+---+----+----------+------------+----------+
## |garbage          |No |350|Inf | 2.0476928|-0.252765807|-2.3312039|
## |                 |Yes|179|Inf | 3.2015843| 0.490022496|-1.3449091|
## +-----------------+---+---+----+----------+------------+----------+
## |crack            |No |436|Inf | 2.1620451|-0.371176035|-2.6790627|
## |                 |Yes| 93|Inf | 3.8177123| 2.508437147|-0.2814125|
## +-----------------+---+---+----+----------+------------+----------+
## |leaning_wall     |No |497|Inf | 2.2591000|-0.116832917|-2.1255326|
## |                 |Yes| 32|Inf |       Inf| 2.708050201|-0.1251631|
## +-----------------+---+---+----+----------+------------+----------+
## |tree             |No |217|Inf | 1.8691461|-0.518205731|-2.1323639|
## |                 |Yes|312|Inf | 2.7932080| 0.349673748|-1.7805862|
## +-----------------+---+---+----+----------+------------+----------+
## |downward_floor   |No |470|Inf | 2.1972246|-0.248072934|-2.2454267|
## |                 |Yes| 59|Inf |       Inf| 4.060443011|-0.4480247|
## +-----------------+---+---+----+----------+------------+----------+
## |tilted           |No |436|Inf | 2.1375053|-0.399734433|-2.4386134|
## |                 |Yes| 93|Inf | 4.5217886| 3.102342009|-0.5978370|
## +-----------------+---+---+----+----------+------------+----------+
## |ground_veg       |No |150|Inf | 1.3451363|-1.265666373|-2.5365787|
## |                 |Yes|379|Inf | 3.1218141| 0.445205437|-1.7315003|
## +-----------------+---+---+----+----------+------------+----------+
## |scars            |No |318|Inf | 1.7771607|-1.398128819|-4.1367653|
## |                 |Yes|211|Inf | 5.3471075| 3.000719815|-0.8540775|
## +-----------------+---+---+----+----------+------------+----------+
## |mixed            |No |488|Inf | 2.2626685|-0.082013152|-2.0430739|
## |                 |Yes| 41|Inf | 3.6888795| 1.003302109|-0.8823892|
## +-----------------+---+---+----+----------+------------+----------+
## |conc_rainfall    |No | 17|Inf |-0.6061358|-2.772588722|      -Inf|
## |                 |Yes|512|Inf | 2.5818989| 0.054701136|-1.8763169|
## +-----------------+---+---+----+----------+------------+----------+
## |wastewater       |No |199|Inf | 1.6522579|-0.460215623|-2.9391619|
## |                 |Yes|330|Inf | 3.0445224| 0.268263987|-1.5453591|
## +-----------------+---+---+----+----------+------------+----------+
## |angle            |C  | 30|Inf |       Inf|-0.268263987|      -Inf|
## |                 |D  |132|Inf | 3.7612001| 0.762140052|-1.3581235|
## |                 |E  |367|Inf | 1.9934627|-0.246471804|-2.0733253|
## +-----------------+---+---+----+----------+------------+----------+
## |banana           |No |354|Inf | 1.9785928|-0.295926142|-2.2749336|
## |                 |Yes|175|Inf | 3.7553692| 0.600253434|-1.3862944|
## +-----------------+---+---+----+----------+------------+----------+
## |drainage         |Y  | 71|Inf | 0.9360934|-1.929909808|-4.2484952|
## |                 |P  |231|Inf | 2.2512918|-0.448950220|-2.5980493|
## |                 |N  |227|Inf | 3.7932395| 0.978811089|-1.2386584|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_saprolite_soil|No |440|Inf | 2.2753894|-0.100083459|-2.0541237|
## |                 |Yes| 89|Inf | 2.6270811| 0.480972661|-1.3723081|
## +-----------------+---+---+----+----------+------------+----------+
## |TC               |No | 31|Inf |       Inf| 0.597837001|-1.9095425|
## |                 |Yes|498|Inf | 2.2613197|-0.040166042|-1.9141615|
## +-----------------+---+---+----+----------+------------+----------+
## |deforestation    |No |491|Inf | 2.3183690| 0.044814016|-1.8979009|
## |                 |Yes| 38|Inf | 2.4567358|-0.653926467|-2.1400662|
## +-----------------+---+---+----+----------+------------+----------+
## |Overall          |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +-----------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=1, cex.axis=2, cex.sub=1)

Equation 3

parameters okay and so/so
porportion
p-value based equation 2 > 0.5

f3 <- lrm(risk ~ angle +building + EN + DepTaludeAterro+ DepTaludeCorte+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall_water+ wastewater+ tree + TC , data=train.data, x=TRUE , y=TRUE) stargazer(anova(f3), type=“text”, style=“default”)

# x=TRUE, y=TRUE used by resid() below 

eq_OLR_03 <- polr(risk ~ wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage, data=train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_03))


p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                          Value Std. Error    t value      p value
## woodTRUE            1.15648174  0.3185486  3.6304724 1.414515e-04
## TC_mature_soilTRUE  0.62400820  0.2152496  2.8989980 1.871787e-03
## T_constructionTRUE  0.75432691  0.2874781  2.6239459 4.345879e-03
## landfillTRUE       -0.16666490  0.2924984 -0.5697977 2.844074e-01
## crackTRUE           1.61467343  0.3136191  5.1485177 1.312765e-07
## leaning_wallTRUE    1.54595590  0.4810655  3.2136077 6.553931e-04
## treeTRUE           -0.46139881  0.2281912 -2.0219832 2.158904e-02
## downward_floorTRUE  0.86008313  0.3569470  2.4095539 7.986018e-03
## tiltedTRUE          0.87868289  0.3180930  2.7623458 2.869383e-03
## ground_vegTRUE      0.87649783  0.2665764  3.2879800 5.045450e-04
## scarsTRUE           3.59485212  0.3307962 10.8672704 8.253272e-28
## conc_rainfallTRUE   1.48533326  0.6003969  2.4739187 6.682004e-03
## wastewaterTRUE      0.69634674  0.2315639  3.0071472 1.318560e-03
## bananaTRUE          0.30843014  0.2383932  1.2937873 9.786945e-02
## drainage.L          0.72110787  0.2652309  2.7187930 3.276030e-03
## drainage.Q          0.03998779  0.1845090  0.2167255 4.142112e-01
## R1|R2               0.77628657  0.5833004  1.3308522 9.161882e-02
## R2|R3               5.06168730  0.6389593  7.9217674 1.170790e-15
## R3|R4               9.73234897  0.7584622 12.8316855 5.448374e-38

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## woodTRUE           1.16     0.32     3.63   0.0001 
## TC_mature_soilTRUE 0.62     0.22     2.90    0.002 
## T_constructionTRUE 0.75     0.29     2.62    0.004 
## landfillTRUE       -0.17    0.29     -0.57   0.28  
## crackTRUE          1.61     0.31     5.15   0.0000 
## leaning_wallTRUE   1.55     0.48     3.21    0.001 
## treeTRUE           -0.46    0.23     -2.02   0.02  
## downward_floorTRUE 0.86     0.36     2.41    0.01  
## tiltedTRUE         0.88     0.32     2.76    0.003 
## ground_vegTRUE     0.88     0.27     3.29    0.001 
## scarsTRUE          3.59     0.33     10.87     0   
## conc_rainfallTRUE  1.49     0.60     2.47    0.01  
## wastewaterTRUE     0.70     0.23     3.01    0.001 
## bananaTRUE         0.31     0.24     1.29    0.10  
## drainage.L         0.72     0.27     2.72    0.003 
## drainage.Q         0.04     0.18     0.22    0.41  
## R1| R2             0.78     0.58     1.33    0.09  
## R2| R3             5.06     0.64     7.92      0   
## R3| R4             9.73     0.76     12.83     0   
## ---------------------------------------------------

#print (f3 , latex =TRUE , coefs =5)

par(mfrow=c(3,5))
plot.xmean.ordinaly (risk ~  wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage,,
          data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+----------+------------+----------+
## |              |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +--------------+---+---+----+----------+------------+----------+
## |wood          |No |458|Inf | 2.2167851|-0.157530203|-2.2670784|
## |              |Yes| 71|Inf | 3.5409593| 1.079920156|-0.6097656|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |257|Inf | 1.9151385|-0.274076420|-2.3685431|
## |              |Yes|272|Inf | 2.9139023| 0.251314428|-1.5918936|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |220|Inf | 1.5349354|-0.804372816|-2.9444390|
## |              |Yes|309|Inf | 3.6276687| 0.550726841|-1.4863778|
## +--------------+---+---+----+----------+------------+----------+
## |landfill      |No |329|Inf | 1.8423117|-0.502728414|-2.5422579|
## |              |Yes|200|Inf | 4.5951199| 0.847297860|-1.2656664|
## +--------------+---+---+----+----------+------------+----------+
## |crack         |No |436|Inf | 2.1620451|-0.371176035|-2.6790627|
## |              |Yes| 93|Inf | 3.8177123| 2.508437147|-0.2814125|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.2591000|-0.116832917|-2.1255326|
## |              |Yes| 32|Inf |       Inf| 2.708050201|-0.1251631|
## +--------------+---+---+----+----------+------------+----------+
## |tree          |No |217|Inf | 1.8691461|-0.518205731|-2.1323639|
## |              |Yes|312|Inf | 2.7932080| 0.349673748|-1.7805862|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |470|Inf | 2.1972246|-0.248072934|-2.2454267|
## |              |Yes| 59|Inf |       Inf| 4.060443011|-0.4480247|
## +--------------+---+---+----+----------+------------+----------+
## |tilted        |No |436|Inf | 2.1375053|-0.399734433|-2.4386134|
## |              |Yes| 93|Inf | 4.5217886| 3.102342009|-0.5978370|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg    |No |150|Inf | 1.3451363|-1.265666373|-2.5365787|
## |              |Yes|379|Inf | 3.1218141| 0.445205437|-1.7315003|
## +--------------+---+---+----+----------+------------+----------+
## |scars         |No |318|Inf | 1.7771607|-1.398128819|-4.1367653|
## |              |Yes|211|Inf | 5.3471075| 3.000719815|-0.8540775|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 17|Inf |-0.6061358|-2.772588722|      -Inf|
## |              |Yes|512|Inf | 2.5818989| 0.054701136|-1.8763169|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater    |No |199|Inf | 1.6522579|-0.460215623|-2.9391619|
## |              |Yes|330|Inf | 3.0445224| 0.268263987|-1.5453591|
## +--------------+---+---+----+----------+------------+----------+
## |banana        |No |354|Inf | 1.9785928|-0.295926142|-2.2749336|
## |              |Yes|175|Inf | 3.7553692| 0.600253434|-1.3862944|
## +--------------+---+---+----+----------+------------+----------+
## |drainage      |Y  | 71|Inf | 0.9360934|-1.929909808|-4.2484952|
## |              |P  |231|Inf | 2.2512918|-0.448950220|-2.5980493|
## |              |N  |227|Inf | 3.7932395| 0.978811089|-1.2386584|
## +--------------+---+---+----+----------+------------+----------+
## |Overall       |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.6, cex.axis=0.6, cex.sub=0.6)

Equation 4

p-value equation 3 > 0.05 (banana, DepTaludeCorte)
consider proportion
paremeters okay & so/so
fashion order

f4 <- lrm(risk ~ building + EN
+ DepEncNatural
+ crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + drainage + TC_mature_Soil + TC + +ground_veg
,data=train.data, x=TRUE , y=TRUE) # x=TRUE, y=TRUE used by resid() below #print (f4 , latex =TRUE , coefs =5) stargazer(anova(f4), type=“text”, style=“default”)

eq_OLR_04 <- polr(risk~ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
                  , data= train.data
           ,  method = "logistic", Hess = TRUE)
p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value

ctable <- coef(summary(eq_OLR_04))

ctable <- cbind(ctable, "p value" = p )

## Warning in cbind(ctable, `p value` = p): number of rows of result is not a
## multiple of vector length (arg 2)

ctable

##                          Value Std. Error    t value      p value
## woodTRUE            1.14758241  0.3181970  3.6065153 1.414515e-04
## TC_mature_soilTRUE  0.63984536  0.2135162  2.9967067 1.871787e-03
## T_constructionTRUE  0.65505791  0.2286261  2.8651934 4.345879e-03
## crackTRUE           1.58826996  0.3096955  5.1284889 2.844074e-01
## leaning_wallTRUE    1.55015405  0.4803406  3.2271980 1.312765e-07
## treeTRUE           -0.45684073  0.2279439 -2.0041804 6.553931e-04
## downward_floorTRUE  0.84385038  0.3558386  2.3714414 2.158904e-02
## tiltedTRUE          0.84197154  0.3112963  2.7047273 7.986018e-03
## ground_vegTRUE      0.86602360  0.2658905  3.2570681 2.869383e-03
## scarsTRUE           3.59185674  0.3303761 10.8720233 5.045450e-04
## conc_rainfallTRUE   1.46566613  0.5982549  2.4499025 8.253272e-28
## wastewaterTRUE      0.71774873  0.2285195  3.1408641 6.682004e-03
## bananaTRUE          0.30541953  0.2383271  1.2815139 1.318560e-03
## drainage.L          0.71938846  0.2653657  2.7109321 9.786945e-02
## drainage.Q          0.03554308  0.1843865  0.1927641 3.276030e-03
## R1|R2               0.76790003  0.5819513  1.3195263 4.142112e-01
## R2|R3               5.04693842  0.6369895  7.9231108 9.161882e-02
## R3|R4               9.71773339  0.7567001 12.8422523 1.170790e-15

stargazer((ctable), type="text", style="default", digits=2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## woodTRUE           1.15     0.32     3.61   0.0001 
## TC_mature_soilTRUE 0.64     0.21     3.00    0.002 
## T_constructionTRUE 0.66     0.23     2.87    0.004 
## crackTRUE          1.59     0.31     5.13    0.28  
## leaning_wallTRUE   1.55     0.48     3.23   0.0000 
## treeTRUE           -0.46    0.23     -2.00   0.001 
## downward_floorTRUE 0.84     0.36     2.37    0.02  
## tiltedTRUE         0.84     0.31     2.70    0.01  
## ground_vegTRUE     0.87     0.27     3.26    0.003 
## scarsTRUE          3.59     0.33     10.87   0.001 
## conc_rainfallTRUE  1.47     0.60     2.45      0   
## wastewaterTRUE     0.72     0.23     3.14    0.01  
## bananaTRUE         0.31     0.24     1.28    0.001 
## drainage.L         0.72     0.27     2.71    0.10  
## drainage.Q         0.04     0.18     0.19    0.003 
## R1| R2             0.77     0.58     1.32    0.41  
## R2| R3             5.05     0.64     7.92    0.09  
## R3| R4             9.72     0.76     12.84     0   
## ---------------------------------------------------

par(mfrow=c(4,4))
plot.xmean.ordinaly (risk ~  wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+----------+------------+----------+
## |              |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +--------------+---+---+----+----------+------------+----------+
## |wood          |No |458|Inf | 2.2167851|-0.157530203|-2.2670784|
## |              |Yes| 71|Inf | 3.5409593| 1.079920156|-0.6097656|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |257|Inf | 1.9151385|-0.274076420|-2.3685431|
## |              |Yes|272|Inf | 2.9139023| 0.251314428|-1.5918936|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |220|Inf | 1.5349354|-0.804372816|-2.9444390|
## |              |Yes|309|Inf | 3.6276687| 0.550726841|-1.4863778|
## +--------------+---+---+----+----------+------------+----------+
## |crack         |No |436|Inf | 2.1620451|-0.371176035|-2.6790627|
## |              |Yes| 93|Inf | 3.8177123| 2.508437147|-0.2814125|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.2591000|-0.116832917|-2.1255326|
## |              |Yes| 32|Inf |       Inf| 2.708050201|-0.1251631|
## +--------------+---+---+----+----------+------------+----------+
## |tree          |No |217|Inf | 1.8691461|-0.518205731|-2.1323639|
## |              |Yes|312|Inf | 2.7932080| 0.349673748|-1.7805862|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |470|Inf | 2.1972246|-0.248072934|-2.2454267|
## |              |Yes| 59|Inf |       Inf| 4.060443011|-0.4480247|
## +--------------+---+---+----+----------+------------+----------+
## |tilted        |No |436|Inf | 2.1375053|-0.399734433|-2.4386134|
## |              |Yes| 93|Inf | 4.5217886| 3.102342009|-0.5978370|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg    |No |150|Inf | 1.3451363|-1.265666373|-2.5365787|
## |              |Yes|379|Inf | 3.1218141| 0.445205437|-1.7315003|
## +--------------+---+---+----+----------+------------+----------+
## |scars         |No |318|Inf | 1.7771607|-1.398128819|-4.1367653|
## |              |Yes|211|Inf | 5.3471075| 3.000719815|-0.8540775|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 17|Inf |-0.6061358|-2.772588722|      -Inf|
## |              |Yes|512|Inf | 2.5818989| 0.054701136|-1.8763169|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater    |No |199|Inf | 1.6522579|-0.460215623|-2.9391619|
## |              |Yes|330|Inf | 3.0445224| 0.268263987|-1.5453591|
## +--------------+---+---+----+----------+------------+----------+
## |banana        |No |354|Inf | 1.9785928|-0.295926142|-2.2749336|
## |              |Yes|175|Inf | 3.7553692| 0.600253434|-1.3862944|
## +--------------+---+---+----+----------+------------+----------+
## |drainage      |Y  | 71|Inf | 0.9360934|-1.929909808|-4.2484952|
## |              |P  |231|Inf | 2.2512918|-0.448950220|-2.5980493|
## |              |N  |227|Inf | 3.7932395| 0.978811089|-1.2386584|
## +--------------+---+---+----+----------+------------+----------+
## |Overall       |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

Equation 5 - Based on Equation 1

based on Eq 1
less p-value > 0.10 (

# x=TRUE, y=TRUE used by resid() below 
#print (f1 , latex =TRUE , coefs =5)
#stargazer(anova(f1), type="text", style="default")

eq_OLR_05 <- polr(risk ~ brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg,  data= train.data
           ,  method = "logistic", Hess = TRUE)

ctable <- coef(summary(eq_OLR_05))

p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                         Value Std. Error    t value      p value
## brickTRUE          -0.8017886  0.4377363 -1.8316702 3.350028e-02
## woodTRUE            1.1613958  0.3177068  3.6555579 1.283116e-04
## TC_mature_soilTRUE  0.6085804  0.2121088  2.8691898 2.057624e-03
## T_constructionTRUE  0.6419487  0.2247481  2.8563036 2.143025e-03
## crackTRUE           1.5777380  0.3068486  5.1417470 1.360978e-07
## leaning_wallTRUE    1.3822538  0.4747772  2.9113738 1.799216e-03
## scarsTRUE           3.6266114  0.3255316 11.1405835 3.979809e-29
## downward_floorTRUE  0.9673063  0.3562411  2.7153137 3.310651e-03
## tiltedTRUE          0.9480315  0.3117904  3.0406052 1.180516e-03
## conc_rainfallTRUE   1.7871103  0.5651682  3.1620856 7.832175e-04
## wastewaterTRUE      0.7985826  0.2225401  3.5884889 1.663001e-04
## ground_vegTRUE      0.9140495  0.2394432  3.8173966 6.743363e-05
## R1|R2               0.5402921  0.7011528  0.7705767 2.204789e-01
## R2|R3               4.7054026  0.7502138  6.2720824 1.781254e-10
## R3|R4               9.3381685  0.8459276 11.0389685 1.239311e-28

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## brickTRUE          -0.80    0.44     -1.83   0.03  
## woodTRUE           1.16     0.32     3.66   0.0001 
## TC_mature_soilTRUE 0.61     0.21     2.87    0.002 
## T_constructionTRUE 0.64     0.22     2.86    0.002 
## crackTRUE          1.58     0.31     5.14   0.0000 
## leaning_wallTRUE   1.38     0.47     2.91    0.002 
## scarsTRUE          3.63     0.33     11.14     0   
## downward_floorTRUE 0.97     0.36     2.72    0.003 
## tiltedTRUE         0.95     0.31     3.04    0.001 
## conc_rainfallTRUE  1.79     0.57     3.16    0.001 
## wastewaterTRUE     0.80     0.22     3.59   0.0002 
## ground_vegTRUE     0.91     0.24     3.82   0.0001 
## R1| R2             0.54     0.70     0.77    0.22  
## R2| R3             4.71     0.75     6.27      0   
## R3| R4             9.34     0.85     11.04     0   
## ---------------------------------------------------

par(mfrow=c(3,4))
plot.xmean.ordinaly (risk ~  brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+----------+------------+----------+
## |              |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +--------------+---+---+----+----------+------------+----------+
## |brick         |No | 33|Inf | 3.4657359| 1.139434283|-0.8329091|
## |              |Yes|496|Inf | 2.2806062|-0.072612533|-2.0217759|
## +--------------+---+---+----+----------+------------+----------+
## |wood          |No |458|Inf | 2.2167851|-0.157530203|-2.2670784|
## |              |Yes| 71|Inf | 3.5409593| 1.079920156|-0.6097656|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |257|Inf | 1.9151385|-0.274076420|-2.3685431|
## |              |Yes|272|Inf | 2.9139023| 0.251314428|-1.5918936|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |220|Inf | 1.5349354|-0.804372816|-2.9444390|
## |              |Yes|309|Inf | 3.6276687| 0.550726841|-1.4863778|
## +--------------+---+---+----+----------+------------+----------+
## |crack         |No |436|Inf | 2.1620451|-0.371176035|-2.6790627|
## |              |Yes| 93|Inf | 3.8177123| 2.508437147|-0.2814125|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.2591000|-0.116832917|-2.1255326|
## |              |Yes| 32|Inf |       Inf| 2.708050201|-0.1251631|
## +--------------+---+---+----+----------+------------+----------+
## |scars         |No |318|Inf | 1.7771607|-1.398128819|-4.1367653|
## |              |Yes|211|Inf | 5.3471075| 3.000719815|-0.8540775|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |470|Inf | 2.1972246|-0.248072934|-2.2454267|
## |              |Yes| 59|Inf |       Inf| 4.060443011|-0.4480247|
## +--------------+---+---+----+----------+------------+----------+
## |tilted        |No |436|Inf | 2.1375053|-0.399734433|-2.4386134|
## |              |Yes| 93|Inf | 4.5217886| 3.102342009|-0.5978370|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 17|Inf |-0.6061358|-2.772588722|      -Inf|
## |              |Yes|512|Inf | 2.5818989| 0.054701136|-1.8763169|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater    |No |199|Inf | 1.6522579|-0.460215623|-2.9391619|
## |              |Yes|330|Inf | 3.0445224| 0.268263987|-1.5453591|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg    |No |150|Inf | 1.3451363|-1.265666373|-2.5365787|
## |              |Yes|379|Inf | 3.1218141| 0.445205437|-1.7315003|
## +--------------+---+---+----+----------+------------+----------+
## |Overall       |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

OLR Equation 6

# x=TRUE, y=TRUE used by resid() below 
#print (f1 , latex =TRUE , coefs =5)
#stargazer(anova(f1), type="text", style="default")

eq_OLR_06 <- polr(risk ~ brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana,  data= train.data
           ,  method = "logistic", Hess = TRUE)

ctable <- coef(summary(eq_OLR_06))

p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                          Value Std. Error    t value      p value
## brickTRUE          -0.86087665  0.5144253 -1.6734727 4.711714e-02
## woodTRUE            1.08535299  0.3213285  3.3777051 3.654671e-04
## mixedTRUE           0.38972759  0.4795220  0.8127418 2.081830e-01
## ENTRUE              0.55318904  0.3658785  1.5119474 6.527362e-02
## TCTRUE              0.38024390  0.4456450  0.8532440 1.967620e-01
## T_constructionTRUE  0.84705850  0.3520257  2.4062406 8.058823e-03
## landfillTRUE       -0.19986692  0.3217770 -0.6211349 2.672554e-01
## leakTRUE           -0.11272431  0.2286103 -0.4930851 3.109762e-01
## garbageTRUE        -0.06498002  0.2846240 -0.2283013 4.097060e-01
## crackTRUE           1.54152875  0.3203237  4.8124095 7.456070e-07
## leaning_wallTRUE    1.49848450  0.4875674  3.0733893 1.058211e-03
## treeTRUE           -0.40806880  0.2299011 -1.7749757 3.795095e-02
## tiltedTRUE          0.97473131  0.3197320  3.0485883 1.149597e-03
## angleD              0.36132644  0.4644558  0.7779566 2.182973e-01
## angleE              0.54417476  0.5232077  1.0400742 1.491527e-01
## ground_vegTRUE      0.94259553  0.2641526  3.5683753 1.796009e-04
## scarsTRUE           3.75664380  0.3300188 11.3831216 2.537502e-30
## conc_rainfallTRUE   1.98575202  0.5738686  3.4602906 2.697964e-04
## wastewaterTRUE      0.74755723  0.2286110  3.2699970 5.377431e-04
## bananaTRUE          0.39123568  0.2412285  1.6218469 5.241806e-02
## R1|R2               1.27656372  1.0519397  1.2135332 1.124630e-01
## R2|R3               5.38728130  1.0835163  4.9720354 3.312681e-07
## R3|R4               9.98448927  1.1662054  8.5615186 5.569520e-18

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## brickTRUE          -0.86    0.51     -1.67   0.05  
## woodTRUE           1.09     0.32     3.38   0.0004 
## mixedTRUE          0.39     0.48     0.81    0.21  
## ENTRUE             0.55     0.37     1.51    0.07  
## TCTRUE             0.38     0.45     0.85    0.20  
## T_constructionTRUE 0.85     0.35     2.41    0.01  
## landfillTRUE       -0.20    0.32     -0.62   0.27  
## leakTRUE           -0.11    0.23     -0.49   0.31  
## garbageTRUE        -0.06    0.28     -0.23   0.41  
## crackTRUE          1.54     0.32     4.81   0.0000 
## leaning_wallTRUE   1.50     0.49     3.07    0.001 
## treeTRUE           -0.41    0.23     -1.77   0.04  
## tiltedTRUE         0.97     0.32     3.05    0.001 
## angleD             0.36     0.46     0.78    0.22  
## angleE             0.54     0.52     1.04    0.15  
## ground_vegTRUE     0.94     0.26     3.57   0.0002 
## scarsTRUE          3.76     0.33     11.38     0   
## conc_rainfallTRUE  1.99     0.57     3.46   0.0003 
## wastewaterTRUE     0.75     0.23     3.27    0.001 
## bananaTRUE         0.39     0.24     1.62    0.05  
## R1| R2             1.28     1.05     1.21    0.11  
## R2| R3             5.39     1.08     4.97   0.0000 
## R3| R4             9.98     1.17     8.56      0   
## ---------------------------------------------------

par(mfrow=c(5,4))
plot.xmean.ordinaly (risk ~  brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+----------+------------+----------+
## |              |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +--------------+---+---+----+----------+------------+----------+
## |brick         |No | 33|Inf | 3.4657359| 1.139434283|-0.8329091|
## |              |Yes|496|Inf | 2.2806062|-0.072612533|-2.0217759|
## +--------------+---+---+----+----------+------------+----------+
## |wood          |No |458|Inf | 2.2167851|-0.157530203|-2.2670784|
## |              |Yes| 71|Inf | 3.5409593| 1.079920156|-0.6097656|
## +--------------+---+---+----+----------+------------+----------+
## |mixed         |No |488|Inf | 2.2626685|-0.082013152|-2.0430739|
## |              |Yes| 41|Inf | 3.6888795| 1.003302109|-0.8823892|
## +--------------+---+---+----+----------+------------+----------+
## |EN            |No |337|Inf | 1.8959830|-0.415366179|-2.2545759|
## |              |Yes|192|Inf | 4.1431347| 0.740400065|-1.4663371|
## +--------------+---+---+----+----------+------------+----------+
## |TC            |No | 31|Inf |       Inf| 0.597837001|-1.9095425|
## |              |Yes|498|Inf | 2.2613197|-0.040166042|-1.9141615|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |220|Inf | 1.5349354|-0.804372816|-2.9444390|
## |              |Yes|309|Inf | 3.6276687| 0.550726841|-1.4863778|
## +--------------+---+---+----+----------+------------+----------+
## |landfill      |No |329|Inf | 1.8423117|-0.502728414|-2.5422579|
## |              |Yes|200|Inf | 4.5951199| 0.847297860|-1.2656664|
## +--------------+---+---+----+----------+------------+----------+
## |leak          |No |354|Inf | 2.0890110|-0.284387177|-2.2749336|
## |              |Yes|175|Inf | 3.0385523| 0.575364145|-1.3862944|
## +--------------+---+---+----+----------+------------+----------+
## |garbage       |No |350|Inf | 2.0476928|-0.252765807|-2.3312039|
## |              |Yes|179|Inf | 3.2015843| 0.490022496|-1.3449091|
## +--------------+---+---+----+----------+------------+----------+
## |crack         |No |436|Inf | 2.1620451|-0.371176035|-2.6790627|
## |              |Yes| 93|Inf | 3.8177123| 2.508437147|-0.2814125|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.2591000|-0.116832917|-2.1255326|
## |              |Yes| 32|Inf |       Inf| 2.708050201|-0.1251631|
## +--------------+---+---+----+----------+------------+----------+
## |tree          |No |217|Inf | 1.8691461|-0.518205731|-2.1323639|
## |              |Yes|312|Inf | 2.7932080| 0.349673748|-1.7805862|
## +--------------+---+---+----+----------+------------+----------+
## |tilted        |No |436|Inf | 2.1375053|-0.399734433|-2.4386134|
## |              |Yes| 93|Inf | 4.5217886| 3.102342009|-0.5978370|
## +--------------+---+---+----+----------+------------+----------+
## |angle         |C  | 30|Inf |       Inf|-0.268263987|      -Inf|
## |              |D  |132|Inf | 3.7612001| 0.762140052|-1.3581235|
## |              |E  |367|Inf | 1.9934627|-0.246471804|-2.0733253|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg    |No |150|Inf | 1.3451363|-1.265666373|-2.5365787|
## |              |Yes|379|Inf | 3.1218141| 0.445205437|-1.7315003|
## +--------------+---+---+----+----------+------------+----------+
## |scars         |No |318|Inf | 1.7771607|-1.398128819|-4.1367653|
## |              |Yes|211|Inf | 5.3471075| 3.000719815|-0.8540775|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 17|Inf |-0.6061358|-2.772588722|      -Inf|
## |              |Yes|512|Inf | 2.5818989| 0.054701136|-1.8763169|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater    |No |199|Inf | 1.6522579|-0.460215623|-2.9391619|
## |              |Yes|330|Inf | 3.0445224| 0.268263987|-1.5453591|
## +--------------+---+---+----+----------+------------+----------+
## |banana        |No |354|Inf | 1.9785928|-0.295926142|-2.2749336|
## |              |Yes|175|Inf | 3.7553692| 0.600253434|-1.3862944|
## +--------------+---+---+----+----------+------------+----------+
## |Overall       |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

Predicion on test data Eq 1: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel1 <- predict(eq_OLR_01, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_01, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel1)

##     predictedLevel1
##      R1 R2 R3 R4
##   R1  3 16  0  0
##   R2  3 85  5  0
##   R3  0 18 59  7
##   R4  0  0 11 17

p1 <- mean(as.character(test.data$risk) != as.character(predictedLevel1)) #misclassification error
p1

## [1] 0.2678571

Predicion on test data Eq 2: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel2 <- predict(eq_OLR_02, test.data) # predict the levels directly

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel2)

##     predictedLevel2
##      R1 R2 R3 R4
##   R1  3 16  0  0
##   R2  4 85  4  0
##   R3  0 18 62  4
##   R4  0  0 11 17

p2 <- mean(as.character(test.data$risk) != as.character(predictedLevel2))
p2

## [1] 0.2544643

Predicion on test data Eq 3: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel3 <- predict(eq_OLR_03, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_03, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel3)

##     predictedLevel3
##      R1 R2 R3 R4
##   R1  3 16  0  0
##   R2  2 85  6  0
##   R3  0 18 62  4
##   R4  0  0 10 18

p3 <- mean(as.character(test.data$risk) != as.character(predictedLevel3))
p3

## [1] 0.25

Predicion on test data Eq 4: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel4 <- predict(eq_OLR_04, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_04, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel4)

##     predictedLevel4
##      R1 R2 R3 R4
##   R1  3 16  0  0
##   R2  2 85  6  0
##   R3  0 19 61  4
##   R4  0  0 10 18

p4 <- mean(as.character(test.data$risk) != as.character(predictedLevel4))
p4

## [1] 0.2544643

Predicion on test data Eq 5: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel5 <- predict(eq_OLR_05, test.data) # predict the levels directly

predictedScores5 <- predict(eq_OLR_05, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel5)

##     predictedLevel5
##      R1 R2 R3 R4
##   R1  3 16  0  0
##   R2  3 84  6  0
##   R3  0 17 60  7
##   R4  0  0  9 19

p5 <- mean(as.character(test.data$risk) != as.character(predictedLevel5))
p5

## [1] 0.2589286

Predicion on test data Eq 6: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel6 <- predict(eq_OLR_06, test.data) # predict the levels directly

predictedScores6 <- predict(eq_OLR_06, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel6)

##     predictedLevel6
##      R1 R2 R3 R4
##   R1  3 16  0  0
##   R2  3 84  6  0
##   R3  0 20 59  5
##   R4  0  0 12 16

p6 <- mean(as.character(test.data$risk) != as.character(predictedLevel6))
p6

## [1] 0.2767857

Predicion on test data Eq 7: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

#Table 

df2 <- data.frame(
  
  "Equations"=c(1:6), 
  "Predicted"=c(1-p1, 
                1-p2,
                1-p3,
                1-p4,
                1-p5,
                1-p6
               
              
    
    
  )
  
  
  
)

df2

##   Equations Predicted
## 1         1 0.7321429
## 2         2 0.7455357
## 3         3 0.7500000
## 4         4 0.7455357
## 5         5 0.7410714
## 6         6 0.7232143

Diagnostic - one model

Erica Goto

2/10/2019

Library & Dataset

Inspect Dataset Using Training and Validation

OLR Equations

Inspect Dataset Using Training and Validation

Equation 1

Creating function with four level

Equation 2

Equation 3

Equation 4

Equation 5 - Based on Equation 1

OLR Equation 6

Predicion on test data Eq 1: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 2: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 3: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 4: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 5: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 6: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 7: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html